INTERNET OF THINGS

The Internet of Things (IoT) is a major driver of new data needs with tens of billions of connected devices today, and growing rapidly; Big Data is getting bigger! The Internet is no longer limited to just IT-related devices like computers, smartphones, and routers.

New device classes are becoming network-enabled en masse:

Sensors.

Industrial and factory machines.

Home appliances.

Home security and automation.

Location-based systems, including drones.

Some of these devices can generate data at a staggering rate – a single sensor may produce data volume on the order of an entire enterprise data warehouse…in a single day. Mobile and networked devices have not only become a significant driver of modern Big Data technologies but are driving evermore innovative and scalable approaches to data management and analytics. Techniques such as stream processing and edge-based filtering and analytics become even more relevant, and tough decisions about ingest and retention policies need to be made.

A holistic solution includes other concerns as well:

Connectivity

How and when does data get to and from edge devices to server infrastructure? How are issues such as bandwidth, intermittent network connections, and protocol mediation handled? How are devices and data secured?

Execution

Edge devices may have limited storage and processing power. Code footprint and efficiency, power, and environmental considerations create a challenging set of design constraints.

Monitoring

What devices are active? Where are they? Which need service? Are there systemic outage patterns that indicate a broader network or platform issue?

Whether you are developing IoT devices, creating an IoT analytics platform to support these devices, or both, BigR.io can provide strategic and technical lift.

Platform Design and Architecture

Evaluation of cloud and data center platform solutions.

Design and implementation of scalable Big Data architectures, such as the Data Lake.

Integration with Existing Enterprise Data Platforms

Ingest logistics.

Storage and capacity planning.

Analytics and machine learning tailored for IoT data.

Operational and decision support.

Application Deployment and Delivery

Design and implementation of embedded IoT device clients.

Adaptability to different device types, configurations, operating systems, and wireless networks and carriers.

Management of application provisioning and support to ensure a cohesive customer experience.

Visionaries in every vertical are predicting that IoT is a catalyst which will create compelling context-aware, location-based applications and change how companies engage with their customers.

New applications are continuously emerging:

Homeowners keeping a virtual eye on the front door.

Mobile insurance apps that measure driver behavior.

A toothbrush that can stream data to a smartphone app and provide real-time feedback on dental habits.

A prescription pill bottle glows or plays a tune when a patient misses a scheduled dose.

Social media may be the first human innovation which pushed data volume beyond technology limits, but machines, not humans, will generate the most data in what’s called IoT (Internet of Things). Every device, including electronics, phones, and network equipment, as well as thermostats, lighting, locks, office equipment, appliances, health monitors, factory equipment and other everyday things (50 billion contributors in all) will add to the mix by 2020.

Thus far, most corporate CIOs have not made IoT their top priority, citing costs, technology challenges, integration risks, security concerns and regulatory issues. However, an explosion of IoT activity is on the horizon, driven by the confluence of low-cost sensors, mobility, and advanced data analytics. Compounding the deluge of information challenges is the near infinite amount of available and affordable cloud-based storage and compute power, aka Big Data.

How does a CIO balance technology challenges with the business opportunities when it comes to IoT adoption? There are a myriad of critical issues:

Identifying business objectives.

Aligning organizational demands and skillsets throughout the enterprise.